15 citations · 32 across the 9 of their papers we have counts for
9 papers
Sepsis Prediction with Temporal Convolutional Networks
Xing Wang, Yuntian He
We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective ana…
Network Topology Optimization via Deep Reinforcement Learning
Zhuoran Li, Xing Wang, Ling Pan +5
Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the c…
Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
Xing Wang, Juan Zhao, Lin Zhu +5
Mobile network traffic forecasting is one of the key functions in daily network operation. A commercial mobile network is large, heterogeneous, complex and dynamic. These intrinsic…
Failure-averse Active Learning for Physics-constrained Systems
Cheolhei Lee, Xing Wang, Jianguo Wu +1
Active learning is a subfield of machine learning that is devised for design and modeling of systems with highly expensive sampling costs. Industrial and engineering systems are ge…
Benchmarking Graph Neural Networks on Link Prediction
Xing Wang, Alexander Vinel
In this paper, we benchmark several existing graph neural network (GNN) models on different datasets for link predictions. In particular, the graph convolutional network (GCN), Gra…
Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network
Xing Wang, Yijun Wang, Bin Weng +1
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market. With representation learning, we derived an embedding called St…
Reannealing of Decaying Exploration Based On Heuristic Measure in Deep Q-Network
Xing Wang, Alexander Vinel
Existing exploration strategies in reinforcement learning (RL) often either ignore the history or feedback of search, or are complicated to implement. There is also a very limited…
Cross Learning in Deep Q-Networks
Xing Wang, Alexander Vinel
In this work, we propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods, particularly in…
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")
Xiang Li, Qitian Chen, Xing Wang +3
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical…